Modeling Cell Migration in Quantitative Image Analysis

Detalhes bibliográficos
Autor(a) principal: Da Silva, Patrícia Andreia Cirne
Data de Publicação: 2011
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/13888
Resumo: All biological phenomena are dynamic and movement is an essential function in cellular systems but their regulation, characteristics and physiological meaning are not fully known. Measurement of the cell movements provides quantitative information that is inevitable for understanding the cellular system. Cell migration is a field of intense current research generating high amounts of image data that need to be quantitatively analyzed with efficiency, consistency and completeness. To accomplish, computerized motion analysis is rapidly becoming a requisite. Since all the existing algorithms for this purpose are often not robust, effective and optimal enough to yield satisfactory results new and alternative methods must be developed. The aim of this work is to find and develop an alternative to the tracking of individual cells in order to, visualize, characterize and quantify the migration characteristics of cell population. This alternative comprises the implementation of a simple and automated algorithm to obtain qualitative and quantitative information from image sequences of cell migration in a fast, easy and inexpensive computationally way. After an extensive literature review, it became clear that all the methodologies and approaches employed to make the quantitative analysis of cell migration only presented solutions that involved object tracking. And the new method developed estimates the probability density functions for cell migration and was implemented as a plugin (Migration) for ImageJ, as cross platform open source application. For the evaluation of the developed algorithm was taken in to account his applicability, efficiency, consistency, completeness and validity. It can be used to process image sequences to extract all information regarding the estimation of the future positions of all particles in a determined time point in the future and is quick when is executing. Comparing to existing approaches to study the cell migration this method adds an improvement, it can deal with complex situation, such as overlapping of particles or other occlusions.
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spelling Modeling Cell Migration in Quantitative Image Analysisparticle trackingpluginImageJquantitative image analysisCell migrationAll biological phenomena are dynamic and movement is an essential function in cellular systems but their regulation, characteristics and physiological meaning are not fully known. Measurement of the cell movements provides quantitative information that is inevitable for understanding the cellular system. Cell migration is a field of intense current research generating high amounts of image data that need to be quantitatively analyzed with efficiency, consistency and completeness. To accomplish, computerized motion analysis is rapidly becoming a requisite. Since all the existing algorithms for this purpose are often not robust, effective and optimal enough to yield satisfactory results new and alternative methods must be developed. The aim of this work is to find and develop an alternative to the tracking of individual cells in order to, visualize, characterize and quantify the migration characteristics of cell population. This alternative comprises the implementation of a simple and automated algorithm to obtain qualitative and quantitative information from image sequences of cell migration in a fast, easy and inexpensive computationally way. After an extensive literature review, it became clear that all the methodologies and approaches employed to make the quantitative analysis of cell migration only presented solutions that involved object tracking. And the new method developed estimates the probability density functions for cell migration and was implemented as a plugin (Migration) for ImageJ, as cross platform open source application. For the evaluation of the developed algorithm was taken in to account his applicability, efficiency, consistency, completeness and validity. It can be used to process image sequences to extract all information regarding the estimation of the future positions of all particles in a determined time point in the future and is quick when is executing. Comparing to existing approaches to study the cell migration this method adds an improvement, it can deal with complex situation, such as overlapping of particles or other occlusions.Coelho, AntónioFalcão, André O.Repositório da Universidade de LisboaDa Silva, Patrícia Andreia Cirne2012-04-26T14:20:44Z20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/13888enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T15:59:20Zoai:repositorio.ul.pt:10451/13888Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:35:49.576068Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Modeling Cell Migration in Quantitative Image Analysis
title Modeling Cell Migration in Quantitative Image Analysis
spellingShingle Modeling Cell Migration in Quantitative Image Analysis
Da Silva, Patrícia Andreia Cirne
particle tracking
plugin
ImageJ
quantitative image analysis
Cell migration
title_short Modeling Cell Migration in Quantitative Image Analysis
title_full Modeling Cell Migration in Quantitative Image Analysis
title_fullStr Modeling Cell Migration in Quantitative Image Analysis
title_full_unstemmed Modeling Cell Migration in Quantitative Image Analysis
title_sort Modeling Cell Migration in Quantitative Image Analysis
author Da Silva, Patrícia Andreia Cirne
author_facet Da Silva, Patrícia Andreia Cirne
author_role author
dc.contributor.none.fl_str_mv Coelho, António
Falcão, André O.
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Da Silva, Patrícia Andreia Cirne
dc.subject.por.fl_str_mv particle tracking
plugin
ImageJ
quantitative image analysis
Cell migration
topic particle tracking
plugin
ImageJ
quantitative image analysis
Cell migration
description All biological phenomena are dynamic and movement is an essential function in cellular systems but their regulation, characteristics and physiological meaning are not fully known. Measurement of the cell movements provides quantitative information that is inevitable for understanding the cellular system. Cell migration is a field of intense current research generating high amounts of image data that need to be quantitatively analyzed with efficiency, consistency and completeness. To accomplish, computerized motion analysis is rapidly becoming a requisite. Since all the existing algorithms for this purpose are often not robust, effective and optimal enough to yield satisfactory results new and alternative methods must be developed. The aim of this work is to find and develop an alternative to the tracking of individual cells in order to, visualize, characterize and quantify the migration characteristics of cell population. This alternative comprises the implementation of a simple and automated algorithm to obtain qualitative and quantitative information from image sequences of cell migration in a fast, easy and inexpensive computationally way. After an extensive literature review, it became clear that all the methodologies and approaches employed to make the quantitative analysis of cell migration only presented solutions that involved object tracking. And the new method developed estimates the probability density functions for cell migration and was implemented as a plugin (Migration) for ImageJ, as cross platform open source application. For the evaluation of the developed algorithm was taken in to account his applicability, efficiency, consistency, completeness and validity. It can be used to process image sequences to extract all information regarding the estimation of the future positions of all particles in a determined time point in the future and is quick when is executing. Comparing to existing approaches to study the cell migration this method adds an improvement, it can deal with complex situation, such as overlapping of particles or other occlusions.
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-01-01T00:00:00Z
2012-04-26T14:20:44Z
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url http://hdl.handle.net/10451/13888
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